<html><body style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space; "><div><br></div><div>Hi, the first paper is a model of skill learning that shows the effects of practice, and second one is a reinforcement learning model on sequence learning. Hope they are useful.</div><div><br></div><div><a href="http://appliedcogsci.vp.uiuc.edu/admin/upload/1283264439Fuetal_HFES06.pdf" target="_blank">Fu,
W.-T., Gonzalez, C, Healy, A., Kole, J., Bourne, L. (2006), Building
predictive human performance models of skill acquisition in a data entry
task. Proceedings of the 50th Annual Meeting of the Human Factors and
Ergonomics Society (pp. 1122-1126). Santa Monica, CA: Human Factors and
Ergonomics Society. </a></div><div><br></div><div><a href="http://appliedcogsci.vp.uiuc.edu/admin/upload/1257300425Fu&Anderson06-JEPG%28published%29%28ReinforcementLearning%29.pdf" target="_blank">Fu,
W.-T., Anderson, J. R. (2006), From recurrent choice to skilled
learning: A reinforcement learning model. Journal of Experimental
Psychology: General, 135(2), 184-206. </a></div><div><br></div><br><div><div>On Aug 30, 2010, at 10:38 AM, Paul J. Reber wrote:</div><br class="Apple-interchange-newline"><blockquote type="cite"><div>This might be just slightly off the general writing/typing topic, but <br>has anybody played around with an ACT-R model of something like playing <br>Guitar Hero? We're using a task something like this in the lab (without <br>music) to look at sequence learning and thinking about the general <br>process of skill acquisition (in perceptual-motor tasks).<br><br>The relation to typing would be why you might be quicker to type <br>familiar words/phrases due to prior practice frequently typing them.<br><br>Paul<br>-- <br>Paul J. Reber, Ph.D.<br>Department of Psychology<br>Northwestern University<br><br>Dan Bothell wrote:<br><blockquote type="cite"><br></blockquote><blockquote type="cite">To test the question about 1 fingered, 2 fingered, and 10 fingered<br></blockquote><blockquote type="cite">typists in ACT-R I created some test models (if you could even call<br></blockquote><blockquote type="cite">them that because they're mostly just Lisp code) which just push motor<br></blockquote><blockquote type="cite">requests through to type out sentences repeatedly for 60 seconds to<br></blockquote><blockquote type="cite">get a words/minute score (where a word is every 5 keypresses). Those<br></blockquote><blockquote type="cite">models were then tested across the three possibilities for pipelining<br></blockquote><blockquote type="cite">of motor actions: "state free", "processor free", and "preparation free".<br></blockquote><blockquote type="cite"><br></blockquote><blockquote type="cite">There were 5 total models:<br></blockquote><blockquote type="cite"><br></blockquote><blockquote type="cite">One-finger is a good "hunt and peck" typist using only one finger.<br></blockquote><blockquote type="cite"><br></blockquote><blockquote type="cite">Two-fingers is a good "hunt and peck" typist using both index fingers<br></blockquote><blockquote type="cite"> keeping each hand on its own side of the keyboard.<br></blockquote><blockquote type="cite"><br></blockquote><blockquote type="cite">Ten-fingers is a model which uses the default press-key action to<br></blockquote><blockquote type="cite"> touch-type using all fingers.<br></blockquote><blockquote type="cite"><br></blockquote><blockquote type="cite">One-finger-savant is a perfect touch-typist using only one index finger<br></blockquote><blockquote type="cite"> i.e. it can move that finger from any key to hit any other key<br></blockquote><blockquote type="cite"> perfectly as a single action, without looking.<br></blockquote><blockquote type="cite"><br></blockquote><blockquote type="cite">Two-finger-savant is a perfect touch-typist using both index fingers<br></blockquote><blockquote type="cite"> where each finger stays on its own side of the keyboard.<br></blockquote><blockquote type="cite"><br></blockquote><blockquote type="cite">Here's the average WPM I got based on 3 simple sentences which<br></blockquote><blockquote type="cite">each have all the letters of the alphabet at least once:<br></blockquote><blockquote type="cite"><br></blockquote><blockquote type="cite"> state free processor free preparation free<br></blockquote><blockquote type="cite">one-finger 13.0 19.1 X<br></blockquote><blockquote type="cite">two-fingers 13.8 20.7 X<br></blockquote><blockquote type="cite">ten-fingers 25.3 40.9 47.5<br></blockquote><blockquote type="cite">one-finger-savant 30.5 44.6 X<br></blockquote><blockquote type="cite">two-finger-savant 28.3 44.1 X<br></blockquote><blockquote type="cite"><br></blockquote><blockquote type="cite">The code is attached if anyone wants to look at the individual<br></blockquote><blockquote type="cite">sentence results (the function run-all-tests will run the models<br></blockquote><blockquote type="cite">through all the conditions), but I wouldn't recommend it as a<br></blockquote><blockquote type="cite">guide for how to write an ACT-R model. :)<br></blockquote><blockquote type="cite"><br></blockquote><blockquote type="cite">Here are the things which I found interesting.<br></blockquote><blockquote type="cite"><br></blockquote><blockquote type="cite">- The fastest overall was the ten fingered model in the "preparation<br></blockquote><blockquote type="cite">free" case at 47.5 wpm, which is faster than I expected.<br></blockquote><blockquote type="cite"><br></blockquote><blockquote type="cite">- Testing "preparation free" actually lead to typing errors for the one-<br></blockquote><blockquote type="cite">and two-fingered models since it was modifying the features before the<br></blockquote><blockquote type="cite">last action had begun (the finger was trying to do two things at once).<br></blockquote><blockquote type="cite">So, those models are skipped for that condition.<br></blockquote><blockquote type="cite"><br></blockquote><blockquote type="cite">- In the other cases the ten fingered model beats the "hunt and peck"<br></blockquote><blockquote type="cite">models as expected, but the "savant" models were faster than the<br></blockquote><blockquote type="cite">ten fingered one. So the savings in preparation time is better than<br></blockquote><blockquote type="cite">the cost of the extra movement relative to the press-key actions<br></blockquote><blockquote type="cite">with the default motor module parameters. However, from a plausibility<br></blockquote><blockquote type="cite">standpoint what those savant models do seems pretty super human to me.<br></blockquote><blockquote type="cite"><br></blockquote><blockquote type="cite"><br></blockquote><blockquote type="cite">Dan<br></blockquote><blockquote type="cite"><br></blockquote><blockquote type="cite"><br></blockquote><blockquote type="cite">------------------------------------------------------------------------<br></blockquote><blockquote type="cite"><br></blockquote><blockquote type="cite">_______________________________________________<br></blockquote><blockquote type="cite">ACT-R-users mailing list<br></blockquote><blockquote type="cite"><a href="mailto:ACT-R-users@act-r.psy.cmu.edu">ACT-R-users@act-r.psy.cmu.edu</a><br></blockquote><blockquote type="cite"><a href="http://act-r.psy.cmu.edu/mailman/listinfo/act-r-users">http://act-r.psy.cmu.edu/mailman/listinfo/act-r-users</a><br></blockquote><br><br><br>_______________________________________________<br>ACT-R-users mailing list<br><a href="mailto:ACT-R-users@act-r.psy.cmu.edu">ACT-R-users@act-r.psy.cmu.edu</a><br>http://act-r.psy.cmu.edu/mailman/listinfo/act-r-users<br></div></blockquote></div><br></body></html>